Customer relations or customer service is an important part of the relationship between a business and existing or potential customers. Such service may include providing information about products and services in which the customer has shown an interest. However, an important part of operating a business is also helping to identify products that may be of interest to a particular customer. Thus customer relations/service may include helping customers to find items that were previously unknown to them but that are believed to be of possible interest. This type of recommendation or a similar service enables a business' employees (such as salespersons) to develop a closer relationship with customers or prospective customers, thereby helping to increase the likelihood that the customer will be satisfied with a purchase. In addition, such services assist the business by improving sales and generating goodwill with customers.
In general, communications between a salesperson and a customer or potential customer are an important part of how a business develops relationships with the public. Many businesses rely on such communications to market products or services, to develop a deeper relationship with existing customers, to develop potential customers into actual customers, and ultimately to increase sales and improve customer retention. In some situations, such communications can serve as part of a larger customer service strategy for a business and assist in delivering a highly personalized experience to a customer or prospective customer. Typically, such communications may be verbal (via phone or in person) or written and delivered using one of several possible delivery methods (e.g., email, text messaging, or printed materials delivered via regular mail).
As mentioned, one aspect of personalized or customized customer services and communications is that of providing a customer or prospective customer with a “recommendation” or “suggestion” as to a product or service that may be of interest to them. The recommendation or suggestion may be based on a salesperson's observations of which items a customer looks at, picks up, tries on in a changing room, etc. While this can be useful and effective in some instances, it is imprecise unless there is a reason to believe that the particular salesperson is somehow very adept at selecting or recommending items for that specific customer. This potential problem can be overcome by using a “personal shopper” or equivalent form of “expert”, but such assistance is typically not available to the casual or less frequent shopper. Most businesses will only offer a personal shopper to those customers who spend a relatively large amount of money on their products or whose use of the products provides the business with intangible benefits (such as increased brand recognition, valuable publicity, etc.). This means that the customer who spends less or whose use of the products does not provide other benefits to the business may be unable to receive the advice of a personal shopper, stylist, or other form of “expert” who might be best able to recommend a product of interest to the customer.
This situation has generated interest in developing effective ways of making recommendations for customers, where the effectiveness may be measured by a conversion rate or other metric that measures how successful an approach was at causing a customer to make a purchase of the recommended item. Conventional approaches to generating a recommendation are typically based on “mining” transaction data for the customer and/or for a class of which the customer is known to (or expected to) share one or more characteristics, where those characteristics are thought to be relevant to selecting an item or items to recommend.
As an example, statistical analysis, machine learning (supervised or unsupervised), or other analytical methods may be used alone or in combination to identify one or more relevant characteristics that the purchasers of an item share. Then data mining can be used to determine a set of items that are typically purchased by members of the group of purchasers. Based on that, a recommendation can be made to a customer who purchased one of the items in the set of items typically (or preferentially) purchased by the group based on collaborative filtering, with the recommendation being to purchase another item in the set. In this example, by having certain shared characteristics with the other members of the group, the customer is assumed to have similar product interests. This assumption may be correct or may be in error, but in many cases, it is the best that can be done without knowing more about the relationship between a person's demographic characteristics and their purchasing preferences. Unfortunately, this approach to generating a recommendation may require a significant amount of transaction data (either from the object of the recommendation or from multiple persons) in order to validate any particular model or assumptions.
Another problem in generating product recommendations arises because many customers shop on-line using an eCommerce web store and the data available about their on-line purchases may be limited. In such a situation, it would be advantageous to be able to generate recommendations based on more than simply the on-line purchases and the information about customer preferences that can be extracted from a limited set of transactions, which in some cases may be all that is available. Further, in some cases a business would like to be able to present a recommendation to a customer or prospective customer relatively early in the customer/vendor relationship and not have to wait until sufficient transaction based data is collected to use in a particular model or algorithm.
One conventional approach to providing a solution to the problem of not being able to have sufficient demographic information to generate a reliable recommendation is based on identifying the IP address corresponding to one or more eCommerce customers. Based on this information, a general geographical location of an identified IP address is linked to aggregated demographics. While a useful solution in theory, the granularity and accuracy of this approach is often poor and the result is therefore too uncertain to rely upon. For example, the IP address could be linked to the location of a city, such as ‘San Francisco’, and then an aggregate demographics profile of San Francisco could be associated with a specific eCommerce customer based on the IP address. However, given the large variety of people living in San Francisco, an aggregation of all of their demographic profiles does not provide a sufficiently accurate characterization of a specific eCommerce customer.
As noted, in a general sense it is known that there are characteristics of customers that are such that if customers have certain of these characteristics, they will be more likely to have an interest in certain products or in a certain set of products. For example, those who vacation in an area where skiing is popular may be more interested in winter clothing. Among others, such characteristics may include age range, income range, education, religious affiliation, ethnicity, nationality, etc. Since some products or services are directed at specific groups, affiliations, or ethnicities (such as foods, makeup, hair products, books, records, movies, etc.), being able to infer that a relatively new purchaser is a member of (or shares something in common with members of) such groups, affiliations, or ethnicities may be helpful in generating more effective recommendations.
While useful and sometimes sufficiently accurate, conventional approaches to generating product recommendations are inherently limited as they typically rely heavily on a set of transactions and the related data. Embodiments of the invention are directed toward solving these and other problems individually and collectively.